Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Author: search.filters.author.Tayfur, GökmenSubject: search.filters.subject.Fly ash

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Generalized Regression Neural Network and Empirical Models To Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag
    (Springer Verlag, 2020) Erdem, Tahir Kemal; Cengiz, Okan; Tayfur, Gökmen
    Gypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Experimental and Modeling Study of Strength of High Strength Concrete Containing Binary and Ternary Binders
    (Foundation Cement, Lime, Concrete, 2011) Erdem, Tahir Kemal; Tayfur, Gökmen; Kırca, Önder
    Silica fume (SF), fl y ash (FA) and ground granulated blastfurnace slag (S) are among the most widely utilized mineral additions for normal strength concrete (NSC) and high strength concrete (HSC). High Reactivity Metakaolin (HRMK) is a relatively new mineral addition, produced by calcination of highly pure kaolin. The replacement of cement with HRMK increases the strength, especially at early ages, and improves durability of concrete. (1-3). Pumice (P) is a porous volcanic glass containing 60-75 SiO2% and 13-17% Al2O3. When fi nely ground, it shows pozzolanic characteristics but it is generally used as a lightweight aggregate in the concrete industry (4, 5). HRMK and P have white color and, therefore, are useful for production of white concrete when applied with white Portland cement (WPC)